Computer Vision

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Computer Vision field includes methods for collecting, processing, interpreting and understanding images, and videos automatically. It mainly focuses on enhancing the ability of a machine. This ability to extract the information means converting the images to textual data, which involves the theory following artificial systems.

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Course Overview

You will learn to work with real-world data to understand its complexities and be capable of building more data. It will also help you to increase your working capabilities on AI projects and will make you capable to build your own AI solutions.

Course Outline


Understand the fundamentals of Computer Vision


This course is aligned to upskill your computer vision knowledge.


Enhance your skill set and boost your career through innovative and independent learning.

Challenges in working with Real World data
Building information on a Dataset
Notebook: Download and Organize Flowers dataset
Notebook: Visualize an Image in Keras
Using Batch generator to avoid Memory error
Exercise: Image Classifier using Batch Generator
Notebook: Image Classifier for Flowers Dataset
Generating data with Image Augmentation
Exercise: Using Image Augmentation for Model training
Notebook: Image Augmentation with Keras
Notebook: Classifier with Image Augmentation
Role of ImageNet in Computer Vision
CNN based Models: AlexNet, ZFNet and VGG16
GooLeNet: Understanding Inception Module
ResNet Architecture
Notebook: ResNet Block in Keras
Understanding Transfer Learning
Exercise: Implementing Transfer Learning
Notebook: Image Classifier using Transfer Learning
Different ways to use Transfer Learning
Exercise: Data Pre-processing for Object Localization
Exercise: Visualizing Bounding Box in an image
Notebook: Pascal Dataset Download & Visualization
Exercise: Image Augmentation in Object Localization
Notebook: Image Augmentation for Localization
Exercise: Building information on Dataset
Exercise: Getting data with Single Objects for Object Localization
Exercise: Building Model and Visualizing predictions
Notebook: Object Localization
Object Localization
R-CNN for Object Detection
What is Object Detection
Faster R-CNN for Detection
Multibox SSD Architecture
Intersection over Union (IoU)
Single Shot Detectors (SSD)
TensorFlow Object Detection API
Notebook: Object Detection API Installation
Selecting Architecture for Model training
Exercise: Preparing data in TFRecord format
Exercise: Creating a Label Mapping file
Notebook: Pascal Dataset - Build Multi Annotations CSV
Notebook: Create TFRecord and Label Mapping file
Exercise: Model Training using Object Detection API
Notebook: Model Training
Exercise: Implement Attention for Training Model
Attention Layer in Seq2Seq Model
Alignment Weights in Attention
Attention in Seq2Seq Model
Exercise: Implement Attention for Prediction Model


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